478 research outputs found
On the Reliability Function of Distributed Hypothesis Testing Under Optimal Detection
The distributed hypothesis testing problem with full side-information is
studied. The trade-off (reliability function) between the two types of error
exponents under limited rate is studied in the following way. First, the
problem is reduced to the problem of determining the reliability function of
channel codes designed for detection (in analogy to a similar result which
connects the reliability function of distributed lossless compression and
ordinary channel codes). Second, a single-letter random-coding bound based on a
hierarchical ensemble, as well as a single-letter expurgated bound, are derived
for the reliability of channel-detection codes. Both bounds are derived for a
system which employs the optimal detection rule. We conjecture that the
resulting random-coding bound is ensemble-tight, and consequently optimal
within the class of quantization-and-binning schemes
Source Coding When the Side Information May Be Delayed
For memoryless sources, delayed side information at the decoder does not
improve the rate-distortion function. However, this is not the case for more
general sources with memory, as demonstrated by a number of works focusing on
the special case of (delayed) feedforward. In this paper, a setting is studied
in which the encoder is potentially uncertain about the delay with which
measurements of the side information are acquired at the decoder. Assuming a
hidden Markov model for the sources, at first, a single-letter characterization
is given for the set-up where the side information delay is arbitrary and known
at the encoder, and the reconstruction at the destination is required to be
(near) lossless. Then, with delay equal to zero or one source symbol, a
single-letter characterization is given of the rate-distortion region for the
case where side information may be delayed or not, unbeknownst to the encoder.
The characterization is further extended to allow for additional information to
be sent when the side information is not delayed. Finally, examples for binary
and Gaussian sources are provided.Comment: revised July 201
Distributed Channel Synthesis
Two familiar notions of correlation are rediscovered as the extreme operating
points for distributed synthesis of a discrete memoryless channel, in which a
stochastic channel output is generated based on a compressed description of the
channel input. Wyner's common information is the minimum description rate
needed. However, when common randomness independent of the input is available,
the necessary description rate reduces to Shannon's mutual information. This
work characterizes the optimal trade-off between the amount of common
randomness used and the required rate of description. We also include a number
of related derivations, including the effect of limited local randomness, rate
requirements for secrecy, applications to game theory, and new insights into
common information duality.
Our proof makes use of a soft covering lemma, known in the literature for its
role in quantifying the resolvability of a channel. The direct proof
(achievability) constructs a feasible joint distribution over all parts of the
system using a soft covering, from which the behavior of the encoder and
decoder is inferred, with no explicit reference to joint typicality or binning.
Of auxiliary interest, this work also generalizes and strengthens this soft
covering tool.Comment: To appear in IEEE Trans. on Information Theory (submitted Aug., 2012,
accepted July, 2013), 26 pages, using IEEEtran.cl
Temporal Lossy In-Situ Compression for Computational Fluid Dynamics Simulations
Während CFD Simulationen für Metallschmelze im Rahmen des SFB920 fallen auf dem Taurus HPC Cluster in Dresden sehr große Datenmengen an, deren Handhabung den wissenschaftlichen Arbeitsablauf stark verlangsamen. Zum einen ist der Transfer in Visualisierungssysteme nur unter hohem Zeitaufwand möglich. Zum anderen ist interaktive Analyse von zeitlich abhängigen Prozessen auf Grund des Speicherflaschenhalses nahezu unmöglich. Aus diesen Gründen beschäftigt sich die vorliegende Dissertation mit der Entwicklung sog. Temporaler In-Situ Kompression für wissenschaftliche Daten direkt innerhalb von CFD Simulationen. Dabei werden mittels neuer Quantisierungsverfahren die Daten auf ~10% komprimiert, wobei dekomprimierte Daten einen Fehler von maximal 1% aufweisen. Im Gegensatz zu nicht-temporaler Kompression, wird bei temporaler Kompression der Unterschied zwischen Zeitschritten komprimiert, um den Kompressionsgrad zu erhöhen. Da die Datenmenge um ein Vielfaches kleiner ist, werden Kosten für die Speicherung und die Übertragung gesenkt. Da Kompression, Transfer und Dekompression bis zu 4 mal schneller ablaufen als der Transfer von unkomprimierten Daten, wird der wissenschaftliche Arbeitsablauf beschleunigt
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